At the end of 2012 I decided to challenge theorists who talked about “prediction” in AGI to get them to try using “prediction” in their real life. If prediction is just another name for “understanding” without providing us with some greater insight into how understanding is attained then what good is it? So I decided to try using prediction in my real life to see how it worked. I made a prediction, based on my feelings, that I would be able to create an AGi program within a year to a year and a half. AGi, with a little ‘i’ is intended to represent some clear advancement in AGI but something that is also clearly less than fully human level AGI. I then decided to use my prediction in a way that would be clear to everyone to show that if prediction might lead to some understanding it would have to do so through an application of a mature integrity of method. So if my predictions do not turn out to be reasonably good then I will need to acknowledge that there was something wrong in theories that founded the basis of my prediction. If my predicted successes do not occur, there must have been something wrong in the theories that led me to have the confidence to make the prediction in the first place. If you fail, then you have to recognize the failure if you want to benefit from the experience. (That makes sense.) Now some people made the claim that I did not understand what they meant by their use of the word prediction in their theories or that my attack was elementary. And someone explained that the kind of prediction that they were thinking about in general intelligence occurs at the neural level and so (I figured they were saying) it is not a conscious process that one can test on their own ego-driven assertions of their ability to make an advancement in the field. Well, I’m sorry but those criticisms are just not good enough because they avoid the challenge without any evidence that they had actually made an advancement that transcended my skepticism. My challenge was simple and something that was feasible. Those kinds of criticisms all look a little too familiar. If the opponent is much more naïve about the problem then you are then his challenge can be dismissed out of hand. There is no need to even think about the criticism, much less take a challenge based on it. But it seems obvious to me that you need some way to test your theories and if you can never meet any of your projections of future accomplishment that were based on those theories then they must be off the fringe.
One of the things I found in taking my own challenge was that it started to lead to some realizations that predictions could be used as benchmarks. Furthermore I discovered that the ‘predictions’ had a tendency to cause certain points of view to crystallize in a way that I found that I had to deal with those ideas more succinctly than I would have if those viewpoints had not been crystalized. Anyway, one of my original predictions was that it might take me 5 months before I started testing my theories out, but, then I realized that if it did take me that long then it would be a sign that something was seriously wrong. And indeed, during the past month, the 3rd month of my project, I did not actually work on my program at all and I do not foresee having the time to work on during the next month. This is a serious failing on my part, and even though I originally foresaw something like this happening, my subsequent realization that the 3rd month was critical to meeting a time table I can now conclude that there is absolutely no way I will meet my original 2 year deadline. It is an extremely negative indicator. However there is one good indicator. During the last few days I developed a new theory that learned specialization is a necessary basis for learned (or true) generalization. While this may seem obvious, or perhaps a bit glib, I believe that it may prove to be a major part of my theory. It is a little different than other theories by which higher general intelligence might be based on specialization. Minsky’s common sense reasoning, for example, was founded on the principle that higher general intelligence would be based on the knowledge of everyday facts. One of Ben Goertzel’s theories was that general AI could be founded on thousands of narrow AI algorithms. (I may not have expressed those theories in just the right way but I think I was close.) My theory is different because it is based on the ability of an AGI program to learn. If an AGI program can learn to effectively utilize many specializations in its interactions with the IO world, then it would be able to generalize that knowledge effectively just because it would (by hypothesis) be able to use those generalities effectively for many of the particular variations that might be needed. The ability to specialize is the both the basis for developing generalizations and the basis for applying generalizations. My use of prediction in my everyday life helped me to crystalize some benchmarks to measure the progress (or the lack of it) I was making with my project but it also helped me crystalize methods of analyzing why my project did not progress as well as I hoped it might. Similarly, I believe that my theory that learned specialization is necessary for learned generalization may help me crystalize stepped tests for AGi. This is important because so far it has seemed like the gradual development of an AGI program has been methodically unsound. If I can make careful tests, starting out with something that I know is feasible and then gradually test incremental improvements then there is a much greater chance that I will be successful than if I had to wait until I had it all figured out. So although my benchmarks of progress are extremely negative so far, I feel as if I have a new idea that will help me get started once I find the time. Jim Bromer ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
